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Record W2121282326 · doi:10.4000/pistes.2165

Work load and job stress: two facets of the same situation? Exploratory study in a gerontology department

2008· article· en· W2121282326 on OpenAlexvenueno aff
Sandrine Cazabat, Béatrice Barthe, Nadine Cascino

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2008
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsWorkloadPsychosocialExploratory researchWork (physics)Stress (linguistics)PsychologyApplied psychologyWork stressProcess (computing)NursingOccupational stressJob stressPerceived exertionJob satisfactionMedicineSocial psychologyComputer sciencePsychiatryEngineeringSociology

Abstract

fetched live from OpenAlex

This article attempts to address the concepts of workload and job stress in a joint way, combining a psychosocial and ergonomic approach. This exploratory study was conducted among nurses in a Gerontology hospital. The objective was to learn why an increase in workload led to an increase in perceived stress levels and also whether nurses adopted a regulatory process based on social support. The Initial results show that the increase in perceived stress scores is associated with an increased number of work aspects considered stressful and a decrease in physical exertion. They show that the lowest stress scores appear when the nurses are provided with help in carrying out their tasks and when their work is not interrupted. Finally, the highest stress scores occur when nurses do not help each other. These results show evidence that the establishment of a regulatory process that is both adaptive and pathogenic. In order to handle a work overload, nurses isolated themselves in order to accomplish their duties and this isolation has an impact on their health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.396
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2008
Admission routes1
Has abstractyes

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